Reranking sorts search results a second time so the most relevant matches end up on top.
Example
The search returns 50 passages; a reranker picks the 5 most relevant, which then go to the AI.
For pros
A reranker (usually a cross-encoder model) scores the question and each result together and is more accurate but slower than the first search. In RAG systems it improves answer quality and saves context.
